Monash University, Institute of Transport Studies: World Transit Research (WTR)
Not a member yet
11112 research outputs found
Sort by
Gender differences in commuting travel mode choices among young adults: A spatial heterogeneity perspective
No full textYoung adults of childbearing age (20–35), who are in the process of forming families and raising children while balancing career development, exhibit diverse commuting preferences shaped by gender and family responsibilities. This group is more influenced by family activity allocation and the built environment, resulting in a closer connection between their commuting mode choices and spatial locations. Understanding the spatial heterogeneity and gender disparities in their commuting mode choices is essential for promoting sustainable transportation policies and gender equality. Taking Guiyang, China as a case study, this study constructs a Geographically Weighted Multinomial Logit (GWMNL) model, integrating the multinomial logit (MNL) model with the geographically weighted regression (GWR), to explore the determinants of commuting mode choices among young adults of childbearing age across different spatial environments from three aspects: socio-economic characteristics, travel characteristics, and built environment features. The findings reveal significant variations in commuting behaviors between males and females across different spatial locations. For example, as working hours increase, males\u27 choices of commuting modes vary depending on their residential location, although cars remain the overall preferred option. In contrast, females predominantly rely on cars, with only a few exceptions in certain areas. Additionally, in suburban and exurban areas with steeper terrains, young females of childbearing age are likely to reduce their car usage, in contrast to males who generally continue to favor cars. Based on these findings, we propose region-specific policy recommendations for both males and females to encourage young adults of childbearing age to adopt sustainable commuting practices
Towards a better understanding of changes in cost per riders for bus routes before and after the COVID-19 pandemic in Montréal, Canada
No full textThe COVID-19 pandemic has severely impacted the finance of transit agencies by reducing farebox revenues. Combined changes in ridership and service operation levels have further transformed the financial efficiency of public-transit services. Understanding how these changes vary between routes is crucial to inform service optimization processes to reduce transit agencies\u27 operational deficits. Using data from the bus network in Montréal, Canada, for 2019 and 2022, we assessed changes in cost per rider at the route-level before and right after the COVID-19 pandemic. We categorized daytime multi-stops bus routes (N = 184) based on the income of the areas they served and their cost per rider across both years to assess diverging temporal and spatial patterns. Our results highlighted that high cost per rider routes were mostly located in the periphery of the study area and in the downtown core and that such patterns worsened following the pandemic, particularly for the downtown core. We observed that routes which served higher income areas tended to have higher cost per rider on average than middle- or low-income ones. We further confirmed this finding by categorizing bus routes by their cost per rider, finding that high cost routes in both 2019 and 2022 tended to be serving higher income areas than other routes. The consideration of both temporal, spatial and socio-economic variation of the cost of bus services provides nuance insight to transportation planners as they aim to optimize bus services while being mindful of potential ridership loss and vertical equity issues
Distributional effects of subway fare surges: Evidence from Beijing
No full textThis study estimates the impact of subway fare increases on ridership and explores the distributional effects across demographic groups. We utilize a natural experiment involving a subway fare surge in China\u27s capital, Beijing. We combine daily subway ridership data by subway lines with household travel survey data collected around the time when fares increased. Based on the regression-discontinuity-in-time research design, we find that the subway fare surge led to a 10.4 % reduction in short-run subway ridership, which corresponds to a price elasticity of −0.090. The heterogeneity analysis indicates that households with higher income, greater travel demand during rush hours, and limited access to other transportation modes have relatively lower price elasticity. We further demonstrate that this price reform brought Beijing subway fares closer to their optimal level. These findings highlight the efficient and distributional consequences of public transit price reforms
Exploring nonlinear and interaction effects of TOD on housing rents using XGBoost
No full textUnderstanding the relationship between transit-oriented development (TOD) and housing rents is crucial for formulating effective TOD strategies and optimizing housing market management. These strategies contribute to a healthy housing market and sustainable urban development. Traditional regression models used in existing studies often fail to capture the nonlinear, and interaction effects of TOD on housing rents. This study addresses these limitations by applying the eXtreme Gradient Boosting (XGBoost) algorithm combined with Shapley Additive Explanations (SHAP) analysis to evaluate the effects of TOD on housing rents within Wuhan\u27s Third Ring Road. Our approach not only identifies key TOD factors such as overall walkability, parking lot density, and commercial density but also uncovers significant nonlinear and threshold effects on housing rents. Moreover, we reveal the intricate interaction effects among key TOD variables, demonstrating how the local impact of one factor can be amplified or diminished by changes in another. This study provides novel insights into the complex mechanisms of TOD impacts on housing rents and offers actionable guidance for crafting targeted urban development strategies that promote urban equity and foster a sustainable housing market
Assessing urban-scale spatiotemporal heterogeneous metro station coverage using multi-source mobility data
No full textAssessing the coverage of metro stations is crucial for evaluating and guiding metro construction. Existing methods mainly rely on surveys to obtain the coverage radii by fitting the first-mile distance distribution of metro passengers, which is costly and time-consuming to capture the spatiotemporal heterogeneity at the urban scale. Daily generated multi-source mobility data offers the possibility of a broad and low-cost assessment. This study proposes a framework to assess the coverage radius of metro stations using metro smart card data and Baidu population heatmap data. First, we build a nested logit model to model travelers\u27 mode choice and station selection behaviors, considering both the competitiveness of the metro over other modes and travelers\u27 sensitivity to first-mile distance. We then establish the relationship between choice probability and metro station inflows, calibrating the parameters through a genetic algorithm-based bi-objective optimization. Finally, we propose a novel metro station coverage assessment method using a distance-decay function that describes the cumulative mode choice proportions. An empirical analysis is conducted using Hangzhou, a sizeable monocentric city in China. The results reveal significant tidal patterns in travel behavior parameters. During the morning peak, suburban travelers rely more on the metro, whereas evening peak reliance is more pronounced among urban center travelers. This aligns with Hangzhou\u27s commuting patterns. Moreover, significant differences occur in attraction patterns between downtown and suburban stations. Suburban metro stations exhibit larger coverage radii due to the lack of convenient alternative transport modes, a result that existing methods fail to capture. This evaluation framework can be extended to other cities, offering valuable insights for enhancing metro services
Exploring mobility of care with measures of accessibility
No full textAccessibility, the ease of interacting with potential opportunities, is an increasingly important tool among transport planners aiming to foster equitable and sustainable cities. However, in accessibility research there is a historical focus on employment destinations that is shaped by a masculinist transportation planning tradition. This paper aims to counter this gendered bias by connecting the Mobility of Care framework, a gender-aware transport planning conceptualisation to an empirical accessibility analysis of care destinations in the City of Hamilton, Canada. Care destinations are all the places one must visit to sustain household needs such shopping, errands, and caring for others. This paper considers access to care across different modes of transport at two travel time thresholds (trips shorter than 15-min and 30-min) using a curated care destination dataset. The accessibility methods used includes the cumulative opportunities measure and a competitive and singly-constrained accessibility measure (spatial availability) for different modes. Overall, results indicate that accessibility by car is exceptionally high across the city, while access by public transit, cycling and foot is relatively low with some exceptions in the inner city. Notably, there are distinctions between both methods: cumulative opportunities illustrates a more optimistic potential interaction landscape for non-car modes, while the spatial availability measure demonstrates a theoretically more realistic spatial distribution of care destination availability of potential interaction. Neighbourhoods with both low spatial availability to care and a high proportion of low-income households are also identified and discussed as areas in need of intervention. The manuscript and analysis is computationally reproducible and openly available. The presented analysis demonstrates methods planners can use to apply a gender-aware lens to accessibility analysis. Further, results can inform policies aiming to encourage sustainable mobility
Understanding bus network delay propagation: Integration of causal inference and complex network theory
No full textBus transport, characterized by a complex network of routes and stops, frequently experiences delays that can affect the entire system\u27s reliability, passenger satisfaction, and operational efficiency. Existing research on bus delay propagation predominantly focuses on the route level. They lack a broader network-level perspective, which is essential for fully understanding the complex interactions and delay propagation. Additionally, previous studies typically rely on correlation-based analysis, which may not adequately uncover the underlying causal mechanisms of bus delay propagation. To understand bus delay propagation in the Public Transport System (PTS), this study employs a causality-based model instead of traditional correlation-based analysis to identify causal relationships between bus stops. We introduce a time-series causal discovery model that integrates temporal and spatial features of stop delays to generate a delay propagation causal graph (DPCG). Then, complex network theory and metrics are used to perform topological analysis on the DPCG and identify key bus stops. The case study is conducted using real-time GTFS data from Stockholm, Sweden. The results indicate that stops with more connections significantly influence delay propagation, and the network displays a distinct community structure with mixed connectivity. Moreover, bus stops exhibit different delay propagation patterns during various time periods. During the morning peak, delays primarily propagate to stops in the inner city due to the commuting surge. In the evening peak, however, delays are more widely distributed across central and suburban areas, reflecting the diversity of after-work travel patterns. The study also reveals that delay propagation extends beyond a single route and affects multiple routes
Uncovering the similarity and heterogeneity of metro stations: From passenger mobility, land use, and streetscapes semantics
No full textUnderstanding the spatial and functional characteristics of metro stations in terms of passenger mobility and built environment is imperative for the transportation system development. This study integrates streetscape visual semantics with traditional mobility and land use factors to provide a multi-dimensional characterization of metro stations. By developing a novel semantic analysis framework, this study identifies distinct clusters of Shanghai Metro stations that share similar characteristics in mobility, location, and visual semantics. The results reveals a strong alignment in the similarity among passenger mobility, land use, and streetscapes. Metro stations in the commercial and entertainment zones of the urban center exhibit a high degree of visual enclosure, activity, and diversity, maintaining high ridership throughout the day. In contrast, residential-oriented stations in suburban transfer hubs display clear commuting patterns and more balanced visual characteristics. Furthermore, our findings highlight “partially similar stations”, which, despite exhibiting similar mobility patterns, reflect heterogeneous land use configurations and streetscapes owing to variations in urban structure and development. These findings contribute to a deeper understanding of the spatial dynamics of metro stations and offer valuable implications for urban planning and metro management
Managing uncertain traffic and societal externalities in a road and rail network: Pricing versus Permits
No full textThis paper studies the relative performance of congestion pricing versus tradable permits for a congested bi-modal transport system with traffic externalities, that affect passengers only, and societal externalities that go beyond the passengers. The point in this case is infection risks, as in the pandemic. We study this relative performance for the case where there are uncertainties on both traffic externalities and societal externalities. Earlier literature considered the relative performance of prices and permits for the case with only stochastic traffic externalities. The numerical results indicate that, given how infection risks are likely to affect social cost functions, pricing regulation can be expected to perform better than a tradable permit scheme when there are uncertainties on private infection cost, especially when the uncertainties on the marginal private infection cost increase. However, the government can enhance the performance of tradable permits by allowing the bankability of permits, or by actively engaging in buying or selling permits to optimize the price in response to the realization of stochastic parameters. The results provide theoretical support for policymakers to choose the optimal instruments to internalize the multiple external costs when uncertainties exist
